\documentclass[11pt,a4paper]{article}
%\usepackage[latin9]{inputenc}
\setcounter{secnumdepth}{0}
%\usepackage[latin1]{inputenc}
%\usepackage{amsmath}
%\usepackage{amsfonts}
%\usepackage{amssymb}
\usepackage{bibentry}
\usepackage{fixltx2e} %to typeset subscript in usual text mode, example: like\textsubscript{this}
\usepackage{url}
\usepackage[numbers]{natbib}

%\author{Jing Deng}

%  \makeatletter  
%  \renewcommand{\@bibunitname}{\jobname.\the\@bibunitauxcnt}
%  \makeatother
%\AtEveryBibitem{\clearfield{note}}    % clears notes

\begin{document}
\nobibliography{dj_publication}

\section{Research proposal}
The proposed research will carry out in three aspects: system identification, Neural-Fuzzy control and model predictive control, and international and industrial collaboration.
\subsection{System identification}
The applicant has been working on advanced model construction methods over 5 years. From his point of view, several challenges are still exist in system identification, such as optimal experimental design, system input selection, curse of dimensionality, dealing with large data, non-parameter modelling, and imbalanced data. Based on the applicant's previous research, he would like to carry out original research in optimal experimental design and non-parameter modelling.

In system identification, the effectiveness of model training relies highly on the training data. Such data is expected to carry sufficient information about the underlying system. However, practically, it is difficult to design the excitation (inputs) signal to properly stimulate the system. Prior experience or Pseudorandom binary sequence (PRBS) might be adopted, but there will be high redundancy in the collected data. Optimal experimental design is the method to select a suitable design criterion based on a specific objectives in system identification. Some popular criteria are D-optimal designs, D\textsubscript{s}-optimal design, L-optimal design, Minimax optimal design, and Bayesian optimal design. The applicant has incorporated A-optimal design criterion into a two-stage selection algorithm to reduce the variance of estimated model parameters \cite{deng2013Aoptimality}. However, it is worth to explorer other criteria and develop useful tools for practical experimental design.

Due to the increasing system complexity, it becomes difficult to built a first principal model. Therefore, data-driven approaches becomes more popular. One key issue in data-driven modelling is to pre-determine a model structure so that its parameters can be estimated based on the data. Although a lots of general model structures are widely applied, such as neural neural network and non-linear polynomial models. In order to produce better approximation capability, those models are usually built complex, which is not suitable for practical implementation. By contrast, non-parameter modelling tries to predict the system behaviour directly based on its response. It doesn't require prior knowledge about the system structure, and a smooth prediction can usually be achieved. The applicant has established a solid collaboration on non-parametric modelling with Prof Erwei Bai from the University of Iowa. 

\subsection{Neural-fuzzy control and model predictive control}
Fuzzy logical control has already shown to have several advantages over traditional PID control, such as greater robustness and stability. It is therefore more suitable for practical applications. Expert knowledge of the underlying process can be embedded into the controller through fuzzy rules. Unfortunately, the increase of the number of controller inputs will exponentially increase the number of fuzzy rules required, which makes it difficult to implement practically. To tackle this problem, a fuzzy set selection method [13] will be employed, which significantly reduces the number of fuzzy rules without losing controllability and stability. It is noted that expert knowledge may be incomplete, and the processing conditions change over time. Thus, neural network is incorporated to the fuzzy system to optimize the fuzzy set and rules from data, leading to a neural-fuzzy (NF) network control. Fuzzy set and rule selection is also important for NF control, and the applicant proposed to adopt two-stage selection algorithm to achieve this purpose. Model predictive control is also under investigation for the applications the applicant has been working on.

\subsection{International and industrial collaboration}
In the past five years, the applicant has established strong collaborations with leading researchers within different department at Queen's University Belfast, such as the joint work with academic staff from Chemical Engineering, biologic science, medical school, food safety, and civil engineering in QUB on data analysis, system modeling and control, and classification. Externally, the applicant has had excellent collaborations with leading researchers from universities and industry in both UK and other parts of the world. For example, through EPSRC funded project on thermal management for plastics industry, he has worked closely with IRC in Bradford (a partner of this project), including mutual visits and meetings, lab experiments, and joint publications. The applicant has also helped his supervisor in applying a recent Knowledge Transfer Partnerships (KTP) project in collaboration with Munster Simms Ltd. (another industrial partner of the proposed project), and now is working as academic support in this KTP project. Internationally, the applicant has established links with leading researchers worldwide, for example from University of Iowa, University of Rhode Island, USA, Nanyang Technological University, Singapore, University of Reading and several Top universities in China. In the proposed research, the collaborations will be further strengthened via carrying out joint researches with existing partners on system identification and intelligent control, including knowledge transfer with academic and industrial partners, producing joint publications, and making joint research proposals; and organizing special issues for international journals and special sessions for international conferences with partners. The applicant will also actively develop new partnerships through the existing collaborators and partners through different networking activities. 

\section{Track Record and Research Statement}
\subsection{Track record}
Dr. Deng received his MSc degree on Control Theory and Applications from Shanghai University in 2008 with distinction, and then he started PhD research at Queen’s University Belfast under the supervision of Prof George Irwin (FREng) and Prof Kang Li, funded by the Oversea Research Studentship (ORS). His PhD research was about developing advanced data-driven approaches for nonlinear system modelling and identification with applications to engine fault detection and polymer extrusion control, in collaboration with the polymer processing research centre (PPRC) at Queen’s. During his three years research, he has published 6 journal papers and 8 conference papers. From 2011, he has been recruited as a postdoctoral research fellow in the PPRC, researching on the development of advanced monitoring and control systems for polymer industry, funded by an EPSRC project on thermal management in the process industries, in collaboration with the Polymer Interdisciplinary Research Centre (IRC) at University of Bradford. The main objective of the project is to develop methods and technologies to facilitate the efficient use of thermal energy in existing polymer processing plant operation and in the design of future plants.  He has developed a new generation of online monitoring methods for both energy consumption and melt stability, and further implemented expert control to improve both energy efficiency and product quality. These novel technologies provide low cost, reliable and flexible energy monitoring approaches without using traditional power meters, and can be easily implemented in harsh industrial environments. The intelligent controller based on fuzzy logic has been developed for both melt pressure and melt temperature to ensure a stable melt stream. All research outputs have been disseminated through publications in leading international journals and conferences. Further, close collaborations with industrial partners (e.g. Greiner Packaging and Cherry Pipes) have been established which ensures the wide acceptance of the new technologies in the industries.

Dr. Deng has also established strong collaborations with international researchers. He has helped with Shanghai University in China to set up a research laboratory on new polymer extrusion, and has produced several joint papers on system identification, extrusion control and energy monitoring. One of the highlights of his collaboration is the winning of a third prize on ‘Soft sensor technology for measuring the melt viscosity of polymer extrusion’ in the 2012 China (International) Transducer \& Sensor Innovation Contest (http://www.sensorcontest.com/Home.aspx) as a key team member supervised by Prof Kang Li.

In order to accelerate the deployment of research knowledge, Dr. Deng has played a key role in a knowledge transfer partnership  project (KTP) which aims to develop innovative embedded monitoring and control systems to increase the efficiency and adaptability of space and water heating systems, in collaboration with industrial partner Munster Simms. He has heavily involved in all stages of the project, from drafting of the proposal to the running of the project, and acting as an academic support.

As a self-motivated researcher, Dr Deng is active in Continuing Professional Development (CPD). Through professional training and self-learning, he has already been granted a ‘Certified Associate in Project Management (CAPM)’ by the Project Management Institute (PMI) and ‘Certified LabVIEW Associate Developer (CLAD)’ by National Instruments. It is clear that the project management skills have significantly improved his work efficiency, and will allow him to manage bigger projects in the future. He will also take training courses in teaching methods in the coming year.

\subsection{Research Statement}
Dr. Jing Deng's research interest includes non-linear system identification, intelligent control, Machine learning, Bayesian inference, heuristic Optimization(Particle Swarm Optimization (PSO), Differential Evolution (DE), and Extreme learning Machine (ELM)), classification (Support Vector Machine (SVM) and Fisher Discriminant Analysis (FDA)), and fault detection and diagnosis, with applications to energy market, power plant, power system monitoring, automotive engine monitoring, polymer extrusion, and stretch-blow moulding.  

His PhD research was on the topic of advanced data-driven approaches for non-linear system modelling and identification. Specifically, the early proposed Fast Recursive Algorithm (FRA) and Two-Stage Selection (TSS) method proposed by Prof. Li and Prof. Irwin have been improved to integrate Bayesian regularisation [\ref{deng2009engine},\ref{deng2011locally},\ref{deng2010fastFRA}] to prevent over-fitting and leave-one-out cross validation for automatic model construction [\ref{deng2011fast},\ref{deng2010fast},\ref{Deng10TwoStageAutomatic}]. To further enhance model generalization capability, some heuristic methods were also embedded in the two-stage selection to optimize the non-linear parameters involved in subset model construction. These include Particle Swarm Optimization (PSO) [\ref{deng2011PSO}], Defferential Evolution (DE) [\ref{deng2012heuristically}] and Extreme Learning Machine (ELM) [\ref{deng2011fast}]. The effectiveness and efficiency of all these advanced methods have been confirmed on both well-known benchmarks and real world data sets from automotive engine and polymer extrusion applications. He has also applied the DE method to optimization power transmission[\ref{Niu2012Improved}].

Dr. Deng's current research is on the development of intelligent control for polymer extrusion. The project aims at reducing energy consumption and improving the product quality. The CompactRIO data acquisition system and LabVIEW from National Instruments (NI) are used for the control system design. During the past one and half years, he has successfully developed novel energy monitoring methods for both thermal heating and the drive motor. These provide low cost, reliable and flexible energy monitoring approaches without using physical power meters. Furthermore, the proposed methods are easy to implement in an industrial environment where energy usage at each component can be obtained without interrupting the processing line. An intelligent controller based on fuzzy logic has also been developed for both melt pressure and melt temperature to ensure a stable melt stream. All research outputs have been disseminated via either international conferences [\ref{deng2012energy},\ref{deng2012meltfuzzy}] or journal publications [\ref{deng2013EnergyQuality},\ref{deng2013Lowcost},\ref{Vera2012Thermal}]. Close relationships with industrial partners (Greiner Packaging and Cherry Pipes) have also been established and this has been a key component in the success of the research. 

\textbf{He is currently at th  e second stage of Royal Academy of Engineering Research fellowship (UK) application.}

\newpage
The top 5 representative papers are indicated by a star at the beginning of each paper.

\section{Publications}
\subsection{Patent}
\begin{enumerate}
\item \bibentry{Deng2008LED}
\end{enumerate}

\subsection{Book}
\begin{enumerate}
\item \bibentry{Deng2012AdvancedBook}
\end{enumerate}

\subsection{Journal papers}
\begin{enumerate}
%\item \bibentry{zhang2013fastmodel}
\item $\star$ \bibentry{deng2013EnergyQuality}\label{deng2013EnergyQuality}
\item \bibentry{deng2013Lowcost}\label{deng2013Lowcost}
\item \bibentry{Vera2012Thermal}\label{Vera2012Thermal}
\item \bibentry{Niu2012Improved}\label{Niu2012Improved}
\item \bibentry{du2012Novel}
\item \bibentry{abeykoon2011new}
\item $\star$ \bibentry{deng2011fast}\label{deng2011fast}
\item $\star$ \bibentry{deng2011PSO} \label{deng2011PSO}
\item $\star$ \bibentry{deng2011locally}\label{deng2011locally}
\item \bibentry{liu2011application}
\item \bibentry{li2010compact}
\item \bibentry{deng2007design}
\end{enumerate}

\subsection{Conference papers}
\begin{enumerate}\addtocounter{enumi}{13}
\item \bibentry{Ihemadu2013Invariant}
\item \bibentry{deng2013Aoptimality}
\item \bibentry{deng2012energy} \label{deng2012energy}
\item \bibentry{du2012efficient}\label{du2012efficient}
\item \bibentry{deng2012heuristically}\label{deng2012heuristically}
\item \bibentry{liu2012polymer}
\item \bibentry{deng2012meltfuzzy} \label{deng2012meltfuzzy}
\item \bibentry{abeykoon2010modellinglsms}
\item \bibentry{abeykoon2010modellingukacc}
\item \bibentry{Deng10_TwoStageAutomatic}  \label{Deng10TwoStageAutomatic}
\item \bibentry{liu2010soft} 
\item \bibentry{deng2010fastFRA}\label{deng2010fastFRA}
\item \bibentry{liu2010improved}
\item $\star$ \bibentry{deng2010fast} \label{deng2010fast}
\item \bibentry{deng2009engine} \label{deng2009engine}
\item \bibentry{Li2008Novel}
\end{enumerate}

\section{Presentations}
\begin{enumerate}
\item \textbf{Conference}, `Heuristically optimized RBF neural model for the control of section weights in stretch blow moulding', The UKACC (United Kingdom Automatic Control Council) International Conference on Control (CONTROL 2012), 3 - 5 Sep 2012, Cardiff, UK.
\item \textbf{Invited lecture}, `System identification and its application to Polymer extrusion', Multidisciplinary Forum at Belfast, 22 July 2012, Belfast, UK.
\item \textbf{Invited conference presentation}, `Development of enhanced extruder control', Sustainable Thermal Energy Management in the process industries International Conference (SusTEM 2011), 25-25 Oct 2011, Newcastle, UK.
\item \textbf{Invited conference presentation}, `Single screw extruder control', Polymer Processing and Engineering conference (PPE 11), 6 Nov 2011, Bradford, UK.
\item \textbf{Conference}, `Fast forward RBF network construction based on particle swarm optimization', International Conference on Life System Modeling and Simulation \& 2010 International Conference on Intelligent Computing for Sustainable Energy and Environment (LSMS \& ICSEE 2010), 18 Sep 2010, Wuxi, China.
\item \textbf{Conference}, `Modelling the effects of operating conditions on motor power consumption in single screw extrusion', International Conference on Life System Modeling and Simulation \& 2010 International Conference on Intelligent Computing for Sustainable Energy and Environment (LSMS \& ICSEE 2010), 18 Sep 2010, Wuxi, China.
\item \textbf{Conference}, `Improved Nonlinear PCA Based on RBF Networks and Principal Curves', International Conference on Life System Modeling and Simulation \& 2010 International Conference on Intelligent Computing for Sustainable Energy and Environment (LSMS \& ICSEE 2010), 19 Sep 2010, Wuxi, China.
\item \textbf{Conference}, `Engine fault detection using a nonlinear FIR model and a locally regularised recursive algorithm', The 20th Irish Signals and Systems Conference (ISSC 2009), 10-11 Jun 2009, Dublin, Ireland.
\item \textbf{Conference}, `Design of OPC server based on USB data acquisition module', The 8th industrial instrumentation and automation conference, Shanghai, China.
\end{enumerate}

\bibliographystyle{IEEEtran}
%\bibliographystyle{plain}
\end{document}